Long-term effects of tillage and nitrogen fertilization on soil C and N fractions in a corn–soybean rotation
Bibliographic record
Abstract
Tillage and nitrogen (N) fertilization can influence soil organic matter (SOM) dynamics, but their interactive effects remain contradictory. A long-term (25 yr) corn (Zea mays L.)-soybean (Glycine max L. Merr.) rotation was used to investigate the effect of tillage [moldboard plow (MP) and no-till (NT)] and N rates (0, 80, and 160 kg N·ha−1) on soil organic carbon (SOC), total N (STN), respiration, and SOM fractions [particulate organic matter (POMC, POMN), mineral-associated organic matter (MAOMC, MAOMN), and microbial biomass (MBC, MBN)]. Results indicate that NT had 27% higher SOC and 24% higher STN than MP in the 0–20 cm depth. Furthermore, SOC and STN stocks (0–20 cm) were 22% and 20% higher, respectively, under NT than MP. There was significant stratification under NT, with a rather uniform distribution under MP. The SOM fractions and soil respiration were 28%–275% and 20%–83% higher at the 0–5 and 5–10 cm depths, respectively, under NT than MP. Interestingly, N fertilizer rate or its interaction with tillage had no impact, except for respiration (tillage × N rate and N rate × depth). Hence, while N addition was required for adequate grain production and increased cumulative plant C and N inputs, our findings indicate that the vertical distribution of SOC, STN, and SOM fractions was affected by tillage, thereby influencing resource accessibility and subsequent dynamics of SOM fractions. Taken together, our results support the adoption of NT and judicious use of N fertilizers for enhancing topsoil SOM storage and fertility under humid temperate conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".